Participants repeatedly chose to invest effort or not in exchange for money for themselves, for a charity they selected, or for a stranger, making 75 choices for each. They then added a number (e.g. 3) to 4 digits for a higher reward 💰, or added 0 for a lower baseline reward 🪙.
We examined their revealed preferences to determine how participants subjectively valued their own cognitive effort when working for themselves, a charity, and a stranger. #econtwitter Like physical effort, people avoided mental effort, and were more willing to work for the self.
Using #computational modeling, we looked at the shape of cognitive effort discounting. Unlike physical effort, which typically discounts the value of rewards parabolically, we found that discounting due to cognitive effort was best described as linear.
However, some participants were more willing to invest #effort for charity and strangers. Using #MachineLearning, we found individuals whose self-representations overlapped highly with their representation of charity or stranger were more willing to invest effort on their behalf.
We trained linear SVM's on behavioural data during decisions for self and others, then tested their accuracy at decoding self from other trials. When self-other overlap is low, decoding accuracy is high. When overlap is high, representations are similar, and accuracy is low 🤖🎯.
We expected individuals with high self-other overlap to be more #prosocial. Thus, we should see a negative correlation between decoding accuracy and prosocial effort. Indeed, we found that for both charity and stranger, higher overlap predicted greater effort on their behalf.
In sum, individuals avoided mental effort at the cost of finanicial reward, especially when earning rewards for others, including a self selected charity. However, people who represented others more similarly to themselves were also more willing to invest effort on their behalf.
It was a pleasure working with @minzlicht and @hauselin on this project🤩, which went through many versions, and improved a lot from reviewer feedback. I certainly learned a lot! You can check out the accepted version of the preprint here #OpenScience: psyarxiv.com/zr3we
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Really excited to share a new In Press article at @PsychScience with @minzlicht and @ZoeLFrancis titled The Experience of Empathy in Everyday life! This is my first first-author publication and I'm thrilled to finally tell you about it😀! Preprint psyarxiv.com/hjuab, thread:
We used quota-sampling and experience sampling to examine everyday experiences of empathy and their connection with subjective well-being and prosocial behaviour in the everyday lives of a sample of U.S. adults that was representative of the population on key demographics 1/8.
Our sample of 246 adults answered 7,343 surveys, yielding a rich dataset we are happy to open to interested researchers (osf.io/y3ud7/). We found some really interesting findings that can inform our understanding of empathy moving forward. 2/8